Dataset Information

A comprehensive and high-resolution genome-wide response of p53 to stress.

ABSTRACT: Tumor suppressor p53 regulates transcription of stress-response genes. Many p53 targets remain undiscovered because of uncertainty as to where p53 binds in the genome and the fact that few genes reside near p53-bound recognition elements (REs). Using chromatin immunoprecipitation followed by exonuclease treatment (ChIP-exo), we associated p53 with 2,183 unsplit REs. REs were positionally constrained with other REs and other regulatory elements, which may reflect structurally organized p53 interactions. Surprisingly, stress resulted in increased occupancy of transcription factor IIB (TFIIB) and RNA polymerase (Pol) II near REs, which was reduced when p53 was present. A subset associated with antisense RNA near stress-response genes. The combination of high-confidence locations for p53/REs, TFIIB/Pol II, and their changes in response to stress allowed us to identify 151 high-confidence p53-regulated genes, substantially increasing the number of p53 targets. These genes composed a large portion of a predefined DNA-damage stress-response network. Thus, p53 plays a comprehensive role in regulating the stress-response network, including regulating noncoding transcription.

SUBMITTER: Chang GS

PROVIDER: S-EPMC4113076 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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A comprehensive and high-resolution genome-wide response of p53 to stress.

Chang Gue Su GS Chen Xiangyun Amy XA Park Bongsoo B Rhee Ho Sung HS Li Pingxin P Han Kang Hoo KH Mishra Tejaswini T Chan-Salis Ka Yim KY Li Yunfei Y Hardison Ross C RC Wang Yanming Y Pugh B Franklin BF

Cell reports 20140717 2

Tumor suppressor p53 regulates transcription of stress-response genes. Many p53 targets remain undiscovered because of uncertainty as to where p53 binds in the genome and the fact that few genes reside near p53-bound recognition elements (REs). Using chromatin immunoprecipitation followed by exonuclease treatment (ChIP-exo), we associated p53 with 2,183 unsplit REs. REs were positionally constrained with other REs and other regulatory elements, which may reflect structurally organized p53 intera ...[more]

PMID: 25043190

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Project description:BackgroundHigh-resolution microarray technology is routinely used in basic research and clinical practice to efficiently detect copy number variants (CNVs) across the entire human genome. A new generation of arrays combining high probe densities with optimized designs will comprise essential tools for genome analysis in the coming years. We systematically compared the genome-wide CNV detection power of all 17 available array designs from the Affymetrix, Agilent, and Illumina platforms by hybridizing the well-characterized genome of 1000 Genomes Project subject NA12878 to all arrays, and performing data analysis using both manufacturer-recommended and platform-independent software. We benchmarked the resulting CNV call sets from each array using a gold standard set of CNVs for this genome derived from 1000 Genomes Project whole genome sequencing data.ResultsThe arrays tested comprise both SNP and aCGH platforms with varying designs and contain between ~0.5 to ~4.6 million probes. Across the arrays CNV detection varied widely in number of CNV calls (4-489), CNV size range (~40 bp to ~8 Mbp), and percentage of non-validated CNVs (0-86%). We discovered strikingly strong effects of specific array design principles on performance. For example, some SNP array designs with the largest numbers of probes and extensive exonic coverage produced a considerable number of CNV calls that could not be validated, compared to designs with probe numbers that are sometimes an order of magnitude smaller. This effect was only partially ameliorated using different analysis software and optimizing data analysis parameters.ConclusionsHigh-resolution microarrays will continue to be used as reliable, cost- and time-efficient tools for CNV analysis. However, different applications tolerate different limitations in CNV detection. Our study quantified how these arrays differ in total number and size range of detected CNVs as well as sensitivity, and determined how each array balances these attributes. This analysis will inform appropriate array selection for future CNV studies, and allow better assessment of the CNV-analytical power of both published and ongoing array-based genomics studies. Furthermore, our findings emphasize the importance of concurrent use of multiple analysis algorithms and independent experimental validation in array-based CNV detection studies.

Dataset Information

A comprehensive and high-resolution genome-wide response of p53 to stress.

Publications

A comprehensive and high-resolution genome-wide response of p53 to stress.

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